read the full code. list every of any type error! just list ...

Prompt

read the full code. list every of any type error! just list the results, nothing else. """CLIP-compatible byte-level BPE tokenizer loaded from verified vocab files.""" from __future__ import annotations import gzip import html import json import re from functools import lru_cache from pathlib import Path import torch from app.constants import CLIP_TOKENIZER_CONTEXT_LEN __all__ = ["CliPTokenizer", "basic_clean", "bytes_to_unicode", "whitespace_clean"] _SOT = "<|startoftext|>" _EOT = "<|endoftext|>" _DEFAULT_SOT_ID = 49406 _DEFAULT_EOT_ID = 49407 _WHITESPACE = re.compile(r"\s+") @lru_cache(maxsize=1) def bytes_to_unicode() -> dict[int, str]: """Build the reversible byte-to-unicode map used by CLIP/GPT-2 BPE. Returns: Mapping of 256 byte values to unicode characters. """ values = ( list(range(ord("!"), ord("~") + 1)) + list(range(ord("\xa1"), ord("\xac") + 1)) + list(range(ord("\xae"), ord("\xff") + 1)) ) extra = [chr(value) for value in range(2**8) if value not in values] characters = [chr(value) for value in values] + extra return dict(zip(range(2**8), characters, strict=True)) def basic_clean(text: str) -> str: """Minimal HTML/entity cleanup used before tokenization. Parameters: text: Raw text. Returns: Cleaned text. """ import ftfy # type: ignore[import-not-found] return ftfy.fix_text(text) def whitespace_clean(text: str) -> str: """Collapse whitespace runs to single spaces. Parameters: text: Input text. Returns: Cleaned text. """ return _WHITESPACE.sub(" ", text).strip() class CliPTokenizer: """Byte-level BPE tokenizer matching the CLIP vocabulary format. Loads `vocab.json` (token->id) and `merges.txt` (BPE merge rules) exported from the OpenAI CLIP ViT-B/16 checkpoint; behavior (SOT/EOT padding to a fixed 77-token context, lowercase, punctuation splitting, byte-level merges) is identical to the reference implementation. """ __slots__ = ( "bpe_ranks", "byte_decoder", "byte_encoder", "cache", "context_len", "decoder", "encoder", "eot_id", "eot_token", "merges", "sot_id", "sot_token", ) def __init__(self, vocab_path: Path, merges_path: Path) -> None: """Load vocabulary and merges from disk. Parameters: vocab_path: Path to clip_bpe_vocab.json. merges_path: Path to clip_bpe_merges.txt. Raises: ValueError: When files are malformed or missing special tokens. """ raw = vocab_path.read_text(encoding="utf-8") if vocab_path.suffix == ".gz": raw = gzip.decompress(raw.encode("utf-8")).decode("utf-8") encoder = json.loads(raw) if not isinstance(encoder, dict): raise TypeError("vocab file must contain a JSON object") self.encoder: dict[str, int] = {str(k): int(v) for k, v in encoder.items()} self.decoder: dict[int, str] = {v: k for k, v in self.encoder.items()} merges_text = merges_path.read_text(encoding="utf-8") if merges_path.suffix == ".gz": merges_text = gzip.decompress(merges_text.encode("utf-8")).decode("utf-8") merge_lines = [line for line in merges_text.split("\n") if line and not line.startswith("#")] self.merges = [tuple(line.split()) for line in merge_lines] if not all(len(pair) == 2 for pair in self.merges): raise ValueError("merges file contains malformed lines") self.bpe_ranks = {pair: index for index, pair in enumerate(self.merges)} self.byte_encoder = bytes_to_unicode() self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} self.cache: dict[str, str] = {} if _SOT not in self.encoder or _EOT not in self.encoder: raise ValueError("vocabulary is missing CLIP special tokens") self.sot_token = _SOT self.eot_token = _EOT self.sot_id = self.encoder[_SOT] self.eot_id = self.encoder[_EOT] self.context_len = CLIP_TOKENIZER_CONTEXT_LEN def _bpe(self, token: str) -> str: """Apply BPE merges to one whitespace-free token. Parameters: token: Byte-encoded token. Returns: The merged symbol string. """ if token in self.cache: return self.cache[token] word = tuple(token) pairs = self._get_pairs(word) if not pairs: return token while True: bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) if bigram not in self.bpe_ranks: break first, second = bigram new_word: list[str] = [] index = 0 while index < len(word): try: first_hit = word.index(first, index) except ValueError: new_word.extend(word[index:]) break new_word.extend(word[index:first_hit]) index = first_hit if word[index] == first and index < len(word) - 1 and word[index + 1] == second: new_word.append(first + second) index += 2 else: new_word.append(word[index]) index += 1 word = tuple(new_word) if len(word) == 1: break pairs = self._get_pairs(word) result = " ".join(word) self.cache[token] = result return result @staticmethod def _get_pairs(word: tuple[str, ...]) -> set[tuple[str, str]]: """Collect adjacent symbol pairs. Parameters: word: Symbol tuple. Returns: Set of adjacent pairs. """ return {(word[i], word[i + 1]) for i in range(len(word) - 1)} def _tokenize_text(self, text: str) -> list[str]: """Run the CLIP text preprocessing + BPE pipeline. Parameters: text: Raw prompt text. Returns: BPE token strings (without SOT/EOT). """ cleaned = whitespace_clean(basic_clean(html.unescape(text))).lower() cleaned = re.sub( r"([.,!?:;(){}\[\]\"'])", r" \1 ", cleaned, ) cleaned = whitespace_clean(cleaned) tokens: list[str] = [] for whitespace_token in cleaned.split(" "): if not whitespace_token: continue encoded = "".join(self.byte_encoder[byte] for byte in whitespace_token.encode("utf-8")) tokens.extend(self._bpe(encoded).split(" ")) return tokens def encode(self, text: str) -> list[int]: """Encode text to a fixed-length id list (SOT, tokens..., EOT, 0-pad). Parameters: text: Prompt text (empty string encodes to SOT+EOT, the true unconditional sequence). Returns: Exactly `context_len` integer ids. """ ids = [self.sot_id] for token in self._tokenize_text(text)[: self.context_len - 2]: token_id = self.encoder.get(token) if token_id is not None: ids.append(token_id) ids.append(self.eot_id) while len(ids) < self.context_len: ids.append(0) return ids def encode_batch(self, texts: list[str]) -> torch.Tensor: """Encode a batch of prompts. Parameters: texts: Prompt strings. Returns: [B, context_len] long tensor. """ return torch.tensor([self.encode(text) for text in texts], dtype=torch.long) def decode(self, ids: list[int]) -> str: """Decode ids back to text (debug helper). Parameters: ids: Token ids without padding. Returns: The decoded string. """ tokens = [self.decoder.get(identifier, "") for identifier in ids] symbols = [] for token in tokens: if token in (_SOT, _EOT, ""): continue symbols.extend(token.split(" ")) bytes_out = bytes(self.byte_decoder.get(symbol, 32) for symbol in symbols) return bytes_out.decode("utf-8", errors="replace").strip() @property def vocab_size(self) -> int: """Return the vocabulary size.""" return len(self.encoder)